Model Predictive Control with Softened Constraints for Hybrid Electric Vehicle

Vu Trieu Minh; Reza Moezzi; Klodian Dhoska

doi:10.15157/IJITIS.2023.6.2.1130-1149

Authors

Vu Trieu Minh Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czechia
Reza Moezzi Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Czechia
Klodian Dhoska Department of Production and Management, Faculty of Mechanical Engineering, Polytechnic University of Tirana, Albania

DOI:

https://doi.org/10.15157/IJITIS.2023.6.2.1130-1149

Keywords:

Parallel hybrid electric vehicle; model predictive control with softened constraints; clutch engagement; tracking speed setpoints and torque; high comfortability; low jerk

Abstract

This research work develops the modelling of a parallel hybrid electric vehicle (HEV) using a fully automated friction clutch connecting the combustion engine and the main electric motor to switch between the pure electric driving mode and the combustion engine driving mode. A new scheme of model predictive control (MPC) with softened constraints for this HEV is developed and applied to control the vehicle speed and torque of the motor and the combustion engine. The MPC scheme with softened constraints can provide better drivability and stability for the hybrid vehicle tracking on desired speeds and needed torques. This MPC can also change the driving modes with fast and smooth clutch engagement. The HEV can track better and faster along the desired speeds and torques amid the dynamic constraints imposed on the states, inputs and outputs. MPC with softened constraints can improve considerably the control system stability and robustness.